Automated or algorithmic securities trading refers to the use of electronic platforms for entering trading orders with an algorithm, without human intervention. The precursors to automated trading were, among all, the computerization of order flows in financial markets in the early 1970s, and the emergence of electronic communication networks with fully electronic execution, developed in the late 1980s and 1990s. During the same period, the minimum tick size decreased, which was the catalyst to changing the market microstructure and encouraging the development of algorithm trading.
Both buy and sell side actors are today widely using such software, still primarily to divide orders, test the market and to execute trades. In the beginning of 2000s, the first algorithmic trading strategies were developed and subsequently some proved to be consistently outperforming human traders, such as the MGD algorithm based on the work of Steven Gjerstad and John Dickhouts, and the ZIP algorithm of Dave Cliff.
The focus of these first algorithmic trading strategies was and still is short term, on temporary mispricing in the markets. Computers could of course find and react to such anomalies more rapidly, examining prices in several markets simultaneously. Hence, the delay between transmission of information from a source and reception of the information at a destination, what is called “latency,” became the key success factor for what has become high-frequency trading (HFT).
The growth in automated trading has substantially changed the micro market structure and increased the liquidity of stock markets. Still, there is no sign that market efficiency has increased. Market risk in terms of volatility remains on at least the same level as when algorithmic trading was introduced. In fact, there are many proponents that claim that HFT has increased risk and that nowadays liquidity to a large extent is illusionary, since it can electronically be withdrawn in milliseconds on the basis of a single indicator. Regardless, the use of automated trading has yet to be exploited in more long term investing. Today, short term automated trading is the leading market segment, in terms of volumes, in global investment management. There is no doubt that it has, so far, increased the short term orientation of stock markets.
Financial investing has a centuries long history and so has the professional management of investors' funds—investment management. Over time, financial markets have been diversifying into different security specific types, such as: (i) Stock markets; (ii) Bond markets; (iii) Commodity markets; (iv) Money markets; (v) Derivatives markets; (vi) Futures markets; (vii) Insurance markets, (viii) Foreign exchange markets. The time perspective of investors in these markets vary to a large extent, from HFTs milliseconds, day-traders, swing traders and short term speculators to longer term investors. For obvious reasons, these different types of investors and their advisors market data and information needs are radically different in content and their analytical methods and models are equally different.
Perhaps the most important market innovation for professional investment management was the introduction of mutual funds, where professionals could achieve economies of scale and scope by wholesaling their advice to investors. The growth in the fund management industry has been extraordinary since the 1970s, with a continuing diversification of new fund strategies and structures.
In contrast to the HFT, the traditional investment management industry has sustained an inherent dependency of the individual or team of asset and fund managers, their craftsmanship and biases. The development of modern finance theory helped to eliminate some of these biases, with professionals using the same portfolio theory and CAPM dependent analytics to diversify and pick stocks. Today most assets and fund managers use algorithms in their practices, but have not catered to rationalize and change their own profession. There is no doubt that this has been to the detriment of their clients, who still pay high fixed and performance fees that, alternatively, can be managed by automation and at a fraction of the cost.
At the same time, the high transaction costs and differentiated availability of stock market and corporate information among professional actors, has effectively blocked the full internationalization of the investment management industry. Even though most public corporations operate in what have become genuinely global markets, stock markets are to this day primarily domestic in character. These dysfunctions of financial markets are, in effect, both reducing private and professional investors' possibilities to diversify and blocking their potential to improve sustained risk-adjusted returns through global and automated investment management services.